Open Source Video Generation Models (2026 Landscape Guide)

LTX models have passed 33 million downloads on Hugging Face. That's one of the clearest signals yet that open source video AI has moved from research curiosity to production infrastructure. The question is no longer whether open source video generation models are ready. It's which one fits your workflow.

This guide covers the top open source video generation models available in 2026, with a comparison table, VRAM guide, and a decision framework for choosing the right one.

Why open source video models are winning in 2026

The economics shifted. Training large video models still requires significant compute. Running them doesn't. When model weights are public, any developer with the right hardware runs inference at near-zero marginal cost per generation. No per-seat subscription. No data routing through a third-party cloud. No vendor lock-in on model behavior.

That shift attracted serious investment. Research labs at Alibaba, Tencent, and Lightricks have all published open weights models in the last two years. The quality gap between open and closed systems has narrowed to the point where most production teams now evaluate open source first.

Open weights vs. fully open source: what the distinction means for you

"Open source" covers a spectrum. Some models release weights with commercial restrictions. Others release training code alongside weights. A few release everything including data. For production use, the most important questions are whether commercial deployment is allowed and whether your data stays local. See our open weights licensing page for a full comparison of license types.

How we evaluated these models

Five factors shaped this comparison: output quality (motion consistency, temporal coherence, prompt adherence), compute requirements (minimum VRAM for usable quality output), license terms (commercial use, attribution, derivative restrictions), native capabilities (audio generation, conditioning types), and community activity (community LoRAs, tooling, active development). Models only available through closed APIs without weight access were excluded.

Top open source video generation models in 2026

LTX-2.5

LTX-2.5 is the current standout for production-grade open source video generation. It's a 22B-parameter model that generates video and audio together in a single forward pass, with a separate decoder for each stream, so sound arrives already aligned to picture rather than being added by a second stage.

The text encoder is a custom Gemma 4 12B, paired with an optional prompt enhancer that expands a short prompt before it is encoded. Conditioning feeds both streams, which is what lets the model produce audio that matches what is happening on screen rather than generic ambience.

It also generates multi-shot sequences. A single run can produce several connected shots that hold character, setting, lighting, and voice across a cut, which none of the other models in this comparison attempt.

Production access: ltx-2-5-fast through the LTX API, or locally via the Python packages (ltx-core, ltx-pipelines, ltx-trainer).

Hardware requirements: 32GB VRAM with FP8 quantization enabled, which puts it within reach of RTX 5090-class hardware. An A100 or H100 is the recommended configuration for full quality.

ComfyUI integration is first-party, with built-in templates rather than a community plugin. The open source package covers model internals (ltx-core), production inference flows (ltx-pipelines), and fine-tuning tools (ltx-trainer).

GitHub: Lightricks/LTX-2 | HuggingFace: Lightricks/LTX-2.5 | Product page: LTX-2.5

WAN 2.2 (Alibaba)

WAN 2.2 is Alibaba's open weights video generation model and one of the most widely deployed alternatives in the open source tooling community. It produces strong motion quality, particularly for dynamic scenes with fast movement. VRAM requirements sit in the same tier as LTX-2.3's standard configuration: 80GB+ for full-quality output.

Notable limitation: no native audio generation. If your workflow requires audio, you'll need a separate audio generation step. License: Apache 2.0, which permits commercial use without restrictions.

Compare: LTX-2.5 vs. WAN 2.2

HunyuanVideo 1.5 (Tencent)

HunyuanVideo 1.5 is Tencent's open weights contribution to the space. Quality is high, and it handles longer clip generation well. The VRAM floor is steep: 80GB+ for the full model without quantization. Fine-tuning support is more limited than LTX-2.3 or WAN 2.2.

Before using HunyuanVideo commercially, check the license carefully. Tencent's terms differ from Apache 2.0 and impose restrictions on certain deployment scenarios. No native audio generation.

Compare: LTX-2.5 vs. HunyuanVideo 1.5

CogVideoX-1.5 (Zhipu AI)

CogVideoX-1.5 is a lighter entry point into the open source video generation models category. It runs on 40GB+ VRAM, performs well on short clips, and integrates cleanly with HuggingFace diffusers, making it a natural fit for teams already in that toolchain. Quality and motion consistency are lower than the larger models for longer or more complex scenes. License: Apache 2.0. No native audio.

Compare: LTX-2.5 vs. CogVideoX-1.5

Open-Sora (HPC-AI Tech)

Open-Sora is the most fully transparent option in this list. Code, training pipeline, and weights are all published under Apache 2.0. Its primary audience is researchers who need to inspect or modify the training process. Output quality is lower than the production-grade models above. Not recommended for workflows where output fidelity is a priority, but useful as a baseline for training experiments.

Side-by-side model comparison

How to choose the right open source video model

Start with three questions: What's your VRAM ceiling? Do you need native audio? And what does your license need to permit?

If synchronized audio-video generation is a requirement, LTX-2.5 is currently the only production-grade open source option. The same is true if your work involves cuts rather than single continuous takes. If audio isn't needed and you want maximum permissiveness with Apache 2.0 terms, WAN 2.2 is the strongest video-only alternative at the 80GB tier. For teams running 40GB hardware who want something practical today, CogVideoX-1.5 is the most accessible option.

For production API workflows, the LTX API runs on LTX-2.5 and handles scaling, billing, and infrastructure, so you call ltx-2-5-fast from any HTTP client without managing GPU servers.

VRAM guide by hardware tier

• 24GB (RTX 4090): Open-Sora with memory optimization. CogVideoX-1.5 with quantization.
• 32GB (RTX 5090): LTX-2.5 with FP8. CogVideoX-1.5 standard.
• 40-48GB (A6000): CogVideoX-1.5 at full config. LTX-2.5 with FP8.
• 80GB+ (H100, A100, H800): All models at full quality. LTX-2.5, WAN 2.2, HunyuanVideo 1.5.

How to run open source video models locally

For LTX-2.5, the fastest path is cloning the repository and using the ltx-pipelines package:

git clone https://github.com/Lightricks/LTX-2.git
cd LTX-2
uv sync
source .venv/bin/activate

Model weights download from HuggingFace. For ComfyUI users, the LTX-2.5 templates ship with ComfyUI directly, backed by the ComfyUI-LTXVideo nodes.

For a full walkthrough, see: How to run a video generation model locally.

Why LTX-2.3 is the pick for production workflows

Native audio is the sharpest differentiator. Every other open source video generation model in this comparison requires a separate audio step. LTX-2.5 generates temporally aligned audio and video from the same forward pass, mapping visual events to audio cues as it goes.

Multi-shot is the second. A single generation can produce several connected shots that keep the same character, setting, and lighting across each cut. Every other model here produces one shot per run, which leaves continuity to reference images and manual assembly.

IC-LoRA support adds a layer of structural control that isn't available in WAN 2.2 or HunyuanVideo: depth maps, pose skeletons, edge maps, and motion tracks can all condition generation through lightweight adapter files, without retraining the base model. The same mechanism extends into editing footage you already have, covering in and out painting, relighting, colour work, and creative upscaling.

For teams who want to explore the open source model before committing to infrastructure, the playground is at console.ltx.io/playground.

Frequently asked questions

Which open source video model should I use in 2026? For production use with native audio or multi-shot sequences, LTX-2.5 is the current standard. For video-only with Apache 2.0 licensing, WAN 2.2 is the strongest alternative. CogVideoX-1.5 is the most accessible option for teams with 40GB hardware.

Can I use these models commercially? It depends on the specific model's license. LTX-2.5, WAN 2.2, and CogVideoX-1.5 all permit commercial use, with different terms. LTX-2.5 is free for companies under $10M ARR. HunyuanVideo has restrictions. Always review the specific license for your deployment scenario, especially for SaaS products generating content at scale.

What's the minimum VRAM for LTX-2.5? 32GB with FP8 quantization, which covers GPUs like the RTX 5090. An A100 or H100 is recommended for full quality.

Is LTX-2.5 open source? Yes. The full codebase, model weights, and training tools are available on GitHub and HuggingFace under the LTX License, which is free for commercial use under $10M ARR.

Conclusion

Open source video generation is past the proof-of-concept phase. The models above run in production pipelines at advertising agencies, game studios, and media companies. The choosing criteria now are real: VRAM tier, audio requirements, license terms, and fine-tuning flexibility.

LTX-2.5 is available on GitHub and HuggingFace. The LTX API gives you the same model without managing GPU infrastructure.